The ability to reliably determine the amount of remaining charge of a battery for use as a power source in, for example, portable electronic goods and electric vehicle transport is highly valued by manufacturers and consumers alike in order to calculate the remaining usage time or available distance for vehicles.
In the case of gasoline vehicles the fuel level can simply be measured, however in electric and hybrid vehicles and in electronic devices because the battery is used as their power source it is more difficult to measure residual energy accumulated in the battery. The state of charge (SOC) of a battery may for example be expressed as a percentage indicator of the amount of capacity remaining in a battery until a recharge is needed, compared to the total capacity provided by that battery.
Supposing SOC0 to be the initial SOC percentage at time t0, the battery's SOC percentage at time t is defined as:
  SOC  =            SOC      0        +          100      *                        ∫                      t            0                    t                ⁢                                            I              ⁡                              (                t                )                                      Qt                    ⁢                                          ⁢          dt                    where I is the current, which is defined as negative for discharging and positive for charging, and Qt is the battery's maximum capacity in Ah.
The initial SOC0 where the battery is considered to be fully charged may be set with reference to the maximum open circuit voltage (OCV) prior to discharging the battery, optionally with reference to resistance and temperature measurements to improve accuracy.
Common methods of gauging the SOC of batteries of various chemistries are based on voltage measurements, where typically the voltage of a battery will fall in relation to its remaining capacity. However, the voltage of a Lithium-Sulfur battery does not drop linearly as the battery is discharged, and typically plateaus for large proportions of the discharge characteristic, meaning that the use of voltage is not well-suited to determining the SOC of Lithium-Sulfur batteries.
Another known method of determining SOC of the battery is to perform coulomb counting from the moment the battery begins to be discharged, so that the charge output by the battery is counted and the remaining charge still within the battery can be calculated. With any battery, coulomb counting is only practical if an initial capacity value is known prior to the start of any discharge, and if no other method of capacity estimation is used, then coulomb counting can only practically be implemented when the battery starts from 100% SOC. In practice a battery may not start its discharge from a fully charged state either because it has been subject to a partial discharge, has not been fully charged, or subject to self-discharge and therefore requires an additional method of determining the start of discharge capacity.
One characteristic of Lithium-Sulfur batteries that has been investigated as a means of SOC determination is a measurable relationship between a battery's internal resistance and its SOC, as identified in previous patent applications such as application US 2009/0055110 (Sion Power). Typically, determination of the battery's resistance is through applying a known current in either charge or discharge, monitoring the change in voltage, and applying specific algorithms to predict the battery SOC.
In practice, determination of the battery's resistance through application of a current source is not so straight forward, as identified in European Patent Application No. 1506497.5 (OXIS Energy et al), since there are other factors relating to the duration of an applied current pulse and response of the battery, used in determining the resistance. These factors are typically based on the characteristics of a Lithium-Sulfur battery under load (or charge). An attempt to address the issues of a Lithium-Sulfur battery's characteristics whilst under load has been addressed in the above mentioned European Patent Application No. 1506497.5 (OXIS Energy et al) using a process of Prediction Error Minimisation and Adaptive Neuro-Fuzzy Inference System SOC estimation, claiming a potential mean error in capacity determination of 5% and maximum error of 14%. However, implementation of this technique requires fairly sophisticated algorithms and suitable processing power to handle the number of calculations required.
It is therefore an object of the invention to provide an improved system for determining the SOC of a Lithium-Sulfur battery.